The present invention relates to a method and an apparatus for cone-beam X-ray computed tomography that include an X-ray source for applying a cone-beam X ray, a two-dimensional X-ray detector for detecting the X ray and a scanner having the detector mounted thereon so that the scanner is rotated around an object to be inspected (hereafter simply called an object) for measuring the data projected from multiple points located around the object and reconstructing a distribution of X-ray attenuation coefficients of the object based on the measured data. FIG. 2 shows a common arrangement of the conventional cone-beam X-ray computed tomography. The conventional cone-beam X-ray computed tomography is arranged to have a measuring unit 1, a data processing unit 2 for processing the measured data, and a unit 18 for preparing geometric distortion correction table. On a scan mechanism 3, an X-ray source 4 is located as opposed to a two-dimensional detector 5 such as an X-ray image intensifier (I. I.) with an object 6 laid midway between the X-ray source 4 and the detector 5. The X-ray source 4 operates to apply a cone-beam X ray 7 to the object 6. The two-dimensional detector 5 operates to detect an intensity of the X ray passed through the object 6. The scan mechanism 3 is measuring the intensity of the X ray passed through the object with the detector 5 as it is rotating around the object 6.
The measured data is digitized and then is sent to the data processing unit 2. The data processing unit 2 operates to correct adverse effects applied on the measured data by a dark current bias and a non-uniform distribution of sensitivity of the detector and logarithmic-transform the corrected data into the projection data in a pre-processing unit 9. This logarithmic transformation is the same as the transformation performed by the conventional fan-beam X-ray computed tomography. Next, a geometric distortion correction unit 10 operates to correct the geometric distortion using the geometric distortion correction table. The geometric distortion basically depends on the characteristic of the two-dimensional detector 5. Of the candidate detectors, the image intensifier may bring about a pincushion distortion far remarkably than any other detector. The occurrence of the pin cushion distortion results from the spherical input surface of the image intensifier. If the projection data contains the geometric distortion, the data does not allow the precise reconstruction of the distribution of the X-ray attenuation coefficients. Hence, the correction for the image geometric distortion is an indispensable pre-processing step. In place, the correction for the image geometric distortion may be executed for the measured data before correcting the adverse effects caused by the dark current bias and the non-uniform distribution of sensitivity of the detector or performing the algorithmic transformation. As an example of the aforementioned geometric distortion correcting method, please refer to the writing: R. Ning, et al. "An Image Intensifier-based Volume Tomographic Angiography Imaging System: Geometric Distortion Correction, (SPIE Vol.2163, Physics of Medical Imaging, pp.199-210, 1994) or the Japanese unexamined laid-open No. Hei 5-28316 (JP-A-5-28316).
On the overall projection data formed by the foregoing pre-processing, the image reconstructor 11 operates to reconstruct a three-dimensional distribution of attenuation coefficients of the object 6 within a field of view. This three-dimensional reconstructed data is subject to a volume rendering process or an imaging process like a maximum intensity projection process in the rendering unit 12. As this kind of reconstructing operation, a Feldkamp's cone-beam reconstructing operation is known. (see the publication: L. A. Feldkamp et al., "Practical Cone-beam Algorithm", J. Opt. Soc. Am. A, Vol.1, No.6, pp. 612-619, 1984).
Of the foregoing processes, the correction for geometric distortion will be discussed in detail. If the image intensifier is used as the two-dimensional detector 5, the typical geometric distortion appears as shown in FIG. 6. The image shown in FIG. 6 is obtained by fixing to the front of the detector (image intensifier) 5 a metal plate having pin holes opened at regularly spaced lattice points as shown in FIG. 7 and imaging a projected image. Such a metal plate is called a hole chart 19. The projected image picked by the above process is called a hole chart projected image. This geometric distortion results from a composite of some causes. The first cause is a geometric distortion of the measuring system. The second cause is a nonplanar input surface of the detector 5. In particular, if the detector 5 uses the image intensifier, the spherical input surface of the image intensifier brings about the pincushion distortion in the projection data. The third cause is a deviation of an electron beam of an electronic camera system contained in the image intensifier, the deviation being caused by an external magnetic field such as geomagnetism.
As a method for correcting an image geometric distortion, it is possible to refer to the correcting method built in the technique known as a realtime DR (Digital Radiograph) that uses the image intensifier as the detector 5. The correction for the image geometric distortion is executed by referring to the geometric distortion correction table. This table is saved in the memory 20 for saving geometric distortion correction table. Later, the description will be oriented to the conventional image geometric distortion correcting method arranged to refer to the correction table. The projected image (containing the image geometric distortion) is represented as Pd(U, V). The projected image from which the image geometric distortion is removed is represented as P(u, v). Between both of the images, the relation of P(u, v)=Pd (U(u,v), V(u, v)) is set up, in which the functions U (u, v) and V (u, v) represent the image geometric distortion. In actual, the variables u and v assume discrete values. Hence, these functions U and V are put into a table, then the image geometric distortion is corrected by looking up the table. This table corresponds to a geometric distortion correction table. More particularly, the image geometric distortion correction is executed by preparing the table U (u, v) and V (u, v) in which (u, v) are assumed as pixel coordinates like u=1, 2, . . . , N.sub.i and v=1, 2, . . . , N.sub.j and deriving P (u, v) by the four-point Lagrange's interpolation as indicated in the expression (1). ##EQU1## where U and V are the maximum integers that do not surpass U(u, v) and V(u, v), respectively.
The geometric distortion correction table U(u, v) and V(u, v) is generated by using the foregoing hole chart projected image in the geometric distortion correction table generator 21. The generation of the correction table will be executed as follows. The prepared geometric distortion correction table is saved in the memory 20 for geometric distortion correction table. At first, the pin hole locations are extracted from the hole chart projected image. For the extraction, a proper image recognition technique is used. In the hole chart projected image, as shown in FIG. 7, a pin hole at the i-th dot in the u direction and the j-th dot in the v direction is represented as h.sub.ij, the location of which is represented as (u.sub.ij, v.sub.ij). In actual, as shown in FIG. 7, the pin holes are not allowed to be at the respective lattice points. For the explanation's sake of convenience, it is assumed that the pin holes are located at the respective lattice points. In the projected image containing the image geometric distortion, the pin hole for the pin hole h.sub.ij is H.sub.ij and is located at (U.sub.ij, V.sub.ij). Through the effect of the foregoing functions U(u, v) and V(u, v), the image geometric distortion correction table at this lattice point, that is, the pin hole location is represented as U.sub.ij =U(u.sub.ij, v.sub.ij) and V.sub.ij =V(u.sub.ij, v.sub.ij). The other points except this lattice points (pin hole locations) are defined by the four-point Lagrange's interpolation as indicated in the expression (2). Assuming that u=1, 2, . . . , N.sub.i and v=1, 2, . . . , N.sub.j, the geometric distortion correction table values U(u, v) on all the points (u, v) are defined. The similar operation is performed with respect to V(u, v). ##EQU2## wherein U and V are maximum integers that do not surpass U.sub.ij and V.sub.ij, respectively.
In the cone-beam X-ray computed tomography apparatus, to correct the image geometric distortion, the foregoing geometric distortion correction is executed for the overall projection. In addition, the geometric distortion correction table is generated at least once when the cone-beam X-ray computed tomography apparatus is installed and then at a routine maintenance time.
The foregoing conventional cone-beam X-ray computed tomography apparatus has two shortcomings. One of them is a small field of view of the two-dimensional X-ray detector (simply called a detector), and the other one is a requirement for a massive amount of data for preparing a geometric distortion correction table.
At first, the description will be oriented to the shortcoming resulting from the small field of view of the detector.
In the foregoing conventional apparatus, the field of view of the detector 5 is not large enough to cover the overall object 6. For example, consider that the detector 5 uses the image intensifier. The image intensifier does not have so long a diameter of a plane of incidence. Concretely, it is about ten and some inches (about 30 cm). The object reflected in the field of omnidirectional projections is basically a small spherical region whose diameter is as short as about 20 cm, though the actual region depends on the geometry of the overall measuring system. This restriction of the field of the detector 5 truncates the peripheral portion of the object from the projection data. The projection data with the portion truncated from the actual object is applied to the foregoing Feldkamp's cone-beam reconstructing operation for reconstructing the three-dimensional reconstructed data. In this three-dimensional data, the X-ray attenuation coefficients around the region boundary of the field are made higher than the actual attenuation coefficients, while the attenuation coefficients in the central region of the field are made lower. That is, a shading artifact phenomenon takes place. This reconstructed data does not exactly represent the distribution of the X-ray attenuation coefficients of the object 6, on which data no right diagnosis is allowed. One of the methods for avoiding this shortcoming is that the field of the detector 5 is made large enough to cover the overall object 6. In actual, however, it is difficult to realize the detector 5 with so large a field. The region of interest substantially required for the actual diagnosis is not so large. Hence, if the detector 5 may has a large field of view, the resulting X-ray computed tomography apparatus is made so large in scale and needs a larger amount of calculations. Such an apparatus is not realistic.
In order to avoid occurrence of the shading artifact phenomenon, therefore, it is necessary to realize the method for reconstructing an exact distribution of X-ray attenuation coefficients in the region of interest from the incomplete projection data, that is, the data that does not contain the data about the portion of the object departed from the field of the detector. To realize this kind of method, various studies have been tried. (For example, refer to the writing: R. M. Lewitt., Processing of Incomplete Measurement Data in Computed Tomography, Medical Physics, Vol.6, No.5, pp.412-417, 1979.) The Lewitt's truncation correction is a method for compensating for the data about the portion cut out of the projection data through the effect of the extrapolation. The extrapolation needs information about a rough contour of the object. The contour is assumed as a relatively simple form like an ellipse or measured by a sensor. However, the truncation correction used in the fan-beam X-ray computed tomography is not straightforward applicable to the cone-beam X-ray computed tomography to which the present invention applies. The causes therefor are as follows.
The first cause is a curved surface of incidence of the image intensifier. Further, the influence of geomagnetism does not stabilize the boundary of the field of the detector 5 on the projection data. It is hence difficult to distinguish a site of the object located within the field of the detector 5, that is, the site about which the exact data is measured, from another site of the object outside the field, that is, the site about which the inexact data is measured. As mentioned above, the cone-beam X-ray computed tomography apparatus supplies the projection data after temporarily correcting the geometric distortion of the measured data obtained by the detector 5. The projection data that was subject to the geometric distortion correction does not correspond to the measured data at a one-to-one ratio. Further, the image geometric distortion indicates a pattern varied at each projection (angle). Therefore, on the projection data whose geometric distortion is corrected, the boundary of the field is made variable at projections (angles). On the other hand, the fan-beam X-ray computed tomography apparatus supplies the projection data that corresponds to the data measured by the X-ray detector array at a one-to-one ratio. Therefore, the boundary of the field on the projection data straightforward corresponds to the both ends of the X-ray detector array. It means that the fan-beam X-ray computed tomography apparatus does not have the foregoing shortcoming.
The second cause is difficulty in presuming or measuring the contour of the object. The region of interest to be imaged covers various sites of the object such as a head, a thorx, an abdomen and a pelvis, and each object is different. Hence, it is not easy to presume the contour of the object. Several patterns of the contours may be prepared for allowing the most approximate one to the region of interest of the object to be selected. In this case, the data on which the pattern is selected has to be read by an operator' manual operation, a sensor or the like. Besides, the measurement of the contour for each object makes the overall apparatus idly complicated, and it is not realistic.
The detector 5 like the image intensifier may selectively have a field of view in imaging the object. For a small region of interest, the field of view may be adjusted to be smaller for improving spatial resolution. It is hence desirable to select the truncation correction method, because this method may easily cope with change of a field size if the detector 5 is required to switch the field size when imaging the object.
Next, the description will be oriented to the second shortcoming about a geometric distortion correction table. As mentioned above, the cone-beam X-ray computed tomography apparatus may use the geometric distortion correction method built in the realtime DR technique for the geometric distortion correction for the measured data. In actual, however, the cone-beam X-ray computed tomography needs to process a massive amount of data. Hence, the straightforward application of this correction method may bring about the shortcoming. The first cause (geometric distortion of the measuring system) and the second cause (nonplanar measuring surface of the detector 5), which have been described above as the causes for bringing about the image geometric distortion, do not depend on the direction of the detector 5, that is, the projection angle. However, the image geometric pattern based on the third cause (variable deviation of an electron beam of the electron camera system provided in the image intensifier) varies at each projection angle. It indicates that this correction method needs to prepare the geometric distortion correction tables for all the projection angles. That is, the hole chart projection image is required to be picked at every projection angle. To execute it, however, the following problem rises.
In general, the cone-beam X-ray computed tomography needs to take a hundred to several hundred of images. The conventional fan-beam X-ray computed tomography has already provided the image reconstruction based on more than hundreds of projection images. The cone-beam X-ray computed tomography is often required to process such a number of images. In any case, the cone-beam X-ray computed tomography needs to take such many images and process a massive amount of data. Hence, a certain kind of data abnormality probably takes place in the processing of a signal at the measuring time, transmission of a signal, internal or external storage means or data transfer between storage means (memories). If the data abnormality appears on the hole chart projection image. the abnormality stops to generate the exact geometric distortion correction table. For example, inclusion of noise causes a pin hole to be erroneously recognized at a spot where no pin hole is located. Even one erroneously recognized pin hole may greatly impair the geometric distortion correction table generated on the pin hole. If the geometric distortion correction table is not exactly generated, no exact geometric distortion of the projection data is expected. Hence, the three-dimensional image is not exactly reconstructed.
As means for solving the foregoing problem, improvements for suppressing the abnormality on the image data are added to the measuring system, the signal processing system, the signal transmitting system, the information storage means, and the like. However, these improvements needs to overall or partially redesign the apparatus. This results in making the apparatus more costly. Further, however sophisticated the prepared hardware may be, complete avoidance of abnormal data is impossible.
As means for solving the problem, hence, it is desirable to check if the geometric distortion correction table is exactly generated and replace the table with missing data with a right one at the post-processing step.